科技與工程學院
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沿革
科技與工程學院（原名為科技學院）於87學年度成立，其目標除致力於科技與工程教育師資培育外，亦積極培育與科技產業有關之工程及管理專業人才。學院成立之初在原有之工業教育學系、工業科技教育學系、圖文傳播學系等三系下，自91學年度增設「機電科技研究所」，該所於93學年度起設立學士班並更名為「機電科技學系」。本學院於93學年度亦增設「應用電子科技研究所」，並於96學年度合併工教系電機電子組成立「應用電子科技學系」。此外，「工業科技教育學系」於98學年度更名為「科技應用與人力資源發展學系」朝向培育科技產業之人力資源專才。之後，本院為配合本校轉型之規劃，增加學生於科技與工程產業職場的競爭，本院之「機電科技學系」與「應用電子科技學系」逐漸朝工程技術發展，兩系並於103學年度起分別更名為「機電工程學系」及「電機工程學系」。同年，本學院名稱亦由原「科技學院」更名為「科技與工程學院」。至此，本院發展之重點涵蓋教育（技職教育/科技教育/工程教育）、科技及工程等三大領域，並定位為以技術為本位之應用型學院。
107學年度，為配合本校轉型規劃，「光電科技研究所」由原隸屬於理學院改為隸屬本（科技與工程）學院，另增設2學程，分別為「車輛與能源工程學士學位學程」及「光電工程學士學位學程」。
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Item MIMO Robust Control via Adaptive T-S Merged Fuzzy Models for Nonaffine Nonlinear Systems(2007-01-01) W.-Y. Wang; Y.-H. Chien; I-H. Li; T.-T. LeeItem Adaptive T-S fuzzy controller for a class of general nonlinear systems(2006-01-01) W.-Y. Wang; Y.-H. Chien; I-H. LiItem T-S Fuzzy-Neural Control for Robot Manipulators(2008-08-25) W.-Y. Wang; Y.-H. Chien; Y.-G. Leu; Z.-H. Lee; T.-T. LeeThis paper proposes a novel method of on-line modeling and control through the Takagi-Sugeno (T-S) fuzzy-neural model for a class of general n-link robot manipulators. Compared with the previous method, the main contribution of this paper is an investigation of the more general robot systems using on-line adaptive T-S fuzzy-neural controller. Specifically, the general robot systems are exactly formed a linearized system via the mean value theorem, and then the T-S fuzzy-neural model can approximate the linearized system. Also, we propose an on-line identification algorithm and put significant emphasis on robust tracking controller design using an adaptive scheme for the robot systems. Finally, an example including two cases is provided to demonstrate feasibility and robustness of the proposed method.Item Control of Uncertain Active Suspension System with Antilock Braking system Using Fuzzy Neural Controller(2009-10-14) W.-Y. Wang; M.-C. Chen; Y.-H. Chien; T.-T. LeeThis paper proposes anti-lock braking system to integrate with active suspensions system applied in a quarter vehicles model, and can use a road estimate to get the road condition. This estimate is based on the LuGre friction model with a road condition parameter, and can transmit a reference slip ration to slip ratio controller through a mapping function considering the effect of road characteristics. In the controller design, an observer-based direct adaptive fuzzy-neural controller (DAFC) for an ABS is developed. After, this paper will discuss that active suspension system influence on ABS. Active suspension systems are not ideal, unchanging, and certain, as many control systems assume. If parts of the suspension system fail, it becomes an uncertain system. In such cases, we need an approximator to remodel this uncertain system to maintain good control. We propose a new method to on-line identify the uncertain active suspension system and design a T-S fuzzy-neural controller to control it. Finally, integrating algorithm is constructed to coordinate these two subsystems. Simulation results of the ABS with active suspension system, and is shown to provide good effectiveness under varying conditions.Item On-Line Adaptive T-S Fuzzy Neural Control for Active Suspension Systems(2009-08-24) W.-Y. Wang; M.-C. Chen; Y.-H. Chien; T.-T. LeeVehicles are not always driven on smooth roads. If parts of the suspension system fail, it becomes an uncertain system. Thus we need an approximator to remodel this uncertain system to maintain good control. In this paper, we propose a new method to on-line identify the uncertain suspension system and design a T-S fuzzy-neural controller to control it. We first use the mean value theorem to transform the active suspension system into a virtual linearized system. In addition, an on-line adaptive T-S fuzzy-neural modeling approach to the design of robust tracking controllers is developed for the uncertain active suspension system. Finally, this paper gives simulation results of an uncertain suspension system with the on-line adaptive T-S fuzzy-neural controller, and is shown to provide good effectiveness under the conditions that parts of the suspension system fail.Item An Image-Based Area Measurement System(2011-06-10) C.-T. Chuang; C.-P. Tsai; M.-C. Lu; W.-Y. Wang; Y.-H. ChienA novel image-based area measurement system is proposed in this paper, we can convert the pixels of object into real area by image processes. No matter the system is vertical to the measuring plane or not and the height of system, it could calculates arbitrary two points on the image convert into the actual distance. This paper proposed a new structure for achieve our goal, there are four laser projects and camera fixed on same base, then generate four laser spots on the object or measurement plant, and assume connect each two laser spots as parallel lines, finally it could simulated a ruler in the image. Without consider photography angle for this system, it can measure area of floor or height of building.Item Design and Implementation of FPGA-Based Landslide Detection and Real-Time Monitoring System(2011-11-27) C.-P. Tsai; W.-Y. Wang; M.-C. Lu; Y.-H. Chien; S.-F. SuItem Design of Adaptive T-S Fuzzy-Neural Controller for a Class of Robot Manipulators Using Projection Update Laws(2010-10-13) Y.-H. Chien; W.-Y. Wang; T.-T. LeeAn on-line tracking controller design based on using T-S fuzzy-neural modeling for a class of general robot manipulators is investigated in this paper. Also, we use rojection update laws to tune adjustable parameters for preventing parameters drift. In addition, stability of the closed-loop systems is proven by using strictly-positive-real (SPR) Lyapunov theory. The proposed overall scheme guarantees that all signals involved are bounded and the outputs of the closed-loop system asymptotically track the desired output trajectories. Finally, an example including two cases confirms the effectiveness of the proposed method.Item B-Spline Backstepping Control with Mean-Value Estimation and First-Order Filters(2011-11-27) Y.-H. Chien; W.-Y. Wang; Y.-G. Leu; K.-Y. Lian; T.-T. LeeItem Resarch and Design of Control System for a Tracked Robot with a Kinect Sensor(2012-07-02) N.-H. Fang; I-H. Li; W.-Y. Wang; L.-W. Lee; Y.-H. ChienThe paper displays a tracked robot that is capable of moving on pavement of any types. With Kinect’s in-depth info, the tracked robot can recognize pavement of any special kinds such as a ramp. In this paper, we use the example "recognizing a ramp and climbing it on an unknown pavement" to confirm the way to control a tracked robot. Four modes, a searching mode, a aligning mode, a closing mode and a climbing mode, are proposed to achieve the goal. Intelligent Fuzzy Controller is used here to reduce the cost of computing, increasing efficiency and have an ideal controlling effect.